Julia has strong support for multi-threading and parallel computing. It has built-in primitives like `@threads` and `@spawn` that make it easy to write parallel code. Julia also provides a rich ecosystem of packages for distributed computing, such as `Distributed.jl` and `SharedArrays.jl`, which enable efficient distributed processing. Additionally, the `@distributed` macro allows for easy parallelization of loops across multiple threads or processes in a distributed environment.

Julia's focus on performance and expressiveness makes it a powerful tool for parallel computing. Its ability to seamlessly integrate with existing C, Fortran, and Python code allows for multi-language parallelism, leveraging the strengths of different languages. Overall, Julia provides a flexible and efficient framework for writing parallel and concurrent code.

Julia's multi-threading capabilities are based on its lightweight task model, which allows for efficient scheduling of tasks across multiple threads. It uses a work-stealing algorithm to balance the workload and ensure efficient utilization of all available cores. However, it's important to note that Julia's multi-threading support is still considered experimental and may not provide optimal performance in all scenarios. It's recommended to benchmark and profile your code to determine the best approach to parallelization.